The present invention generally relates to the field of image reconstruction in tomography systems, and more particularly to a method and apparatus for efficient calculation and use of reconstructed pixel variance data in tomography images.
Tomography systems operate by projecting fan shaped or cone shaped X-ray beams through an object. The X-ray beams are generated by an X-ray source, and are generally collimated prior to passing through the object being scanned. The attenuated beams are then detected by a set of detector elements. The detector elements produce a signal based on the intensity of the attenuated X-ray beams, and the signals are processed to produce projection data or images. By using reconstruction techniques such as filtered backprojection, useful images are formed from these projection data.
A computer is able to process and reconstruct images of the portions of the object responsible for the radiation attenuation. As will be appreciated by those skilled in the art, these images are computed by processing a series of angularly displaced projection images. This data is then reconstructed to produce the reconstructed image, which is typically displayed on a cathode ray tube, and may be printed or reproduced on film.
Traditional reconstruction techniques comprise reconstructing the mean number at each pixel. However, there is variability in that value caused by noise processes such as photon noise (X-ray noise), quantization noise and electronic noise in the projection measurements which impact the reconstructed images. It is therefore advantageous not only to reconstruct the mean number in an image, but also the variance associated with each pixel within that image for improved image analysis. In addition, a point-wise variance estimate for each pixel also provides additional diagnostic information about the reconstructed image.
One way of generating a variance image is to take an ensemble of images, reconstruct each image, and then compute the variance for each pixel in the reconstruction over the ensemble of datasets. However, a disadvantage with this technique is that repeated scanning is needed to acquire the multiple datasets, thereby making it computationally inefficient and impractical for clinical applications. A computationally efficient method for determining pixel variance data and generating variance images is therefore desired. It would also be useful to develop ways to use and apply such information, such as in the analysis of reconstructed tomography images, or for improved image acquisition or reconstruction.